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Retrospective monitoring of slope failure event of tailings dam using InSAR time-series observations.

Authors :
Duan, Huizhi
Li, Yongsheng
Jiang, Hongbo
Li, Qiang
Jiang, Wenliang
Tian, Yunfeng
Zhang, Jingfa
Source :
Natural Hazards; Jul2023, Vol. 117 Issue 3, p2375-2391, 17p
Publication Year :
2023

Abstract

Interferometric synthetic aperture radar (InSAR) has been effectively used to monitor surface deformation in a mining area with millimeter-level accuracy. On March 27, 2022, a tailings dam failure occurred in Shanxi Province, China, causing significant damage to surrounding houses, woodland, and roads below the tailings pond. We obtained the surface deformation map of the tailings dam before the catastrophic failure using the InSAR time-series method with a full resolution of Sentinel-1 A. This paper employed a GPU-assisted InSAR processing method for 91 Sentinel-1 images in Interferometric Wide Swath (IW) mode acquired from January 8, 2019, to March 17, 2022. The InSAR results show that during the 25 months preceding the tailings dam failure, Dam-II experienced an average cumulative LOS deformation of nearly 80 mm, while Dam-I experienced a significant deformation of more than 140 mm in the LOS direction. The analysis combining the deformation and rainfall results shows that rainfall significantly affects tailings pond deformation. In general, the deformation evolution has a high correlation with the rainfall annually, but the maximum deformation rate occurs with a delay of about one month compared to the peak rainfall. InSAR technology can significantly improve the monitoring capability of tailings dam failure and other landslide disasters, but it is limited by the observation frequency of the satellite. Thus, improving the temporal resolution of SAR data may assist in predicting tailings dam failure times more accurately. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
0921030X
Volume :
117
Issue :
3
Database :
Complementary Index
Journal :
Natural Hazards
Publication Type :
Academic Journal
Accession number :
164433253
Full Text :
https://doi.org/10.1007/s11069-023-05946-7